Source code for torch.fx.node
# Nodes represent a definition of a value in our graph of operators.
from typing import TYPE_CHECKING, Union, Callable, Any, Tuple, List, Optional, Dict, Set
from .immutable_collections import immutable_dict, immutable_list
import torch
import builtins
import types
from torch.fx.operator_schemas import normalize_function, normalize_module, ArgsKwargsPair
if TYPE_CHECKING:
from .graph import Graph
BaseArgumentTypes = Union[str, int, float, bool, torch.dtype, torch.Tensor, torch.device, torch.memory_format]
base_types = BaseArgumentTypes.__args__ # type: ignore[attr-defined]
Target = Union[Callable[..., Any], str]
Argument = Optional[Union[
Tuple[Any, ...], # actually Argument, but mypy can't represent recursive types
List[Any], # actually Argument
Dict[str, Any], # actually Argument
slice, # Slice[Argument, Argument, Argument], but slice is not a templated type in typing
'Node',
BaseArgumentTypes
]]
_side_effectful_functions: Set[Callable] = {torch._assert}
# this is fixed on master, WAR for 1.5
def _find_module_of_method(orig_method: Callable[..., Any]) -> str:
name = orig_method.__name__
module = orig_method.__module__
if module is not None:
return module
for guess in [torch, torch.nn.functional]:
if getattr(guess, name, None) is orig_method:
return guess.__name__
raise RuntimeError(f'cannot find module for {orig_method}')
# Borrowed from CPython typing module
# https://github.com/python/cpython/blob/f90dc36c15d7fee0efaf6d39e97be0bdf2683e93/Lib/typing.py#L156
def _type_repr(obj):
"""Return the repr() of an object, special-casing types (internal helper).
If obj is a type, we return a shorter version than the default
type.__repr__, based on the module and qualified name, which is
typically enough to uniquely identify a type. For everything
else, we fall back on repr(obj).
"""
# HACK: In Python 3.6, type aliases from ``typing`` are instances of ``type``, but in
# later Python versions, type aliases are not instances of ``type``!! We want
# all type aliases to fall through to ``repr``, so if we have a type that is
# in the module typing, don't go down this path.
if isinstance(obj, type) and obj.__module__ != 'typing':
if obj.__module__ == 'builtins':
return obj.__qualname__
return f'{obj.__module__}.{obj.__qualname__}'
if obj is ...:
return('...')
if isinstance(obj, types.FunctionType):
return obj.__name__
return repr(obj)
def _get_qualified_name(func: Callable[..., Any]) -> str:
# things like getattr just appear in builtins
if getattr(builtins, func.__name__, None) is func:
return func.__name__
name = func.__name__
module = _find_module_of_method(func)
module = module.replace('torch._ops', 'torch.ops') # WAR for bug in how torch.ops assigns module
return f'{module}.{name}'
def _format_arg(arg) -> str:
if isinstance(arg, list):
items = ', '.join(_format_arg(a) for a in arg)
return f'[{items}]'
elif isinstance(arg, tuple):
items = ', '.join(_format_arg(a) for a in arg)
maybe_comma = ',' if len(arg) == 1 else ''
return f'({items}{maybe_comma})'
elif isinstance(arg, dict):
items_str = ', '.join(f'{k}: {_format_arg(v)}' for k, v in arg.items())
return f'{{{items_str}}}'
if isinstance(arg, Node):
return '%' + str(arg)
else:
return str(arg)
[docs]class Node:
"""
``Node`` is the data structure that represents individual operations within
a ``Graph``. For the most part, Nodes represent callsites to various entities,
such as operators, methods, and Modules (some exceptions include nodes that
specify function inputs and outputs). Each ``Node`` has a function specified
by its ``op`` property. The ``Node`` semantics for each value of ``op`` are as follows:
- ``placeholder`` represents a function input. The ``name`` attribute specifies the name this value will take on.
``target`` is similarly the name of the argument. ``args`` holds either: 1) nothing, or 2) a single argument
denoting the default parameter of the function input. ``kwargs`` is don't-care. Placeholders correspond to
the function parameters (e.g. ``x``) in the graph printout.
- ``get_attr`` retrieves a parameter from the module hierarchy. ``name`` is similarly the name the result of the
fetch is assigned to. ``target`` is the fully-qualified name of the parameter's position in the module hierarchy.
``args`` and ``kwargs`` are don't-care
- ``call_function`` applies a free function to some values. ``name`` is similarly the name of the value to assign
to. ``target`` is the function to be applied. ``args`` and ``kwargs`` represent the arguments to the function,
following the Python calling convention
- ``call_module`` applies a module in the module hierarchy's ``forward()`` method to given arguments. ``name`` is
as previous. ``target`` is the fully-qualified name of the module in the module hierarchy to call.
``args`` and ``kwargs`` represent the arguments to invoke the module on, *including the self argument*.
- ``call_method`` calls a method on a value. ``name`` is as similar. ``target`` is the string name of the method
to apply to the ``self`` argument. ``args`` and ``kwargs`` represent the arguments to invoke the module on,
*including the self argument*
- ``output`` contains the output of the traced function in its ``args[0]`` attribute. This corresponds to the "return" statement
in the Graph printout.
"""
def __init__(self, graph: 'Graph', name: str, op: str, target: 'Target',
args: Tuple['Argument', ...], kwargs: Dict[str, 'Argument'],
return_type : Optional[Any] = None) -> None:
self.graph = graph
self.name = name # unique name of value being created
assert op in ['placeholder', 'call_method', 'call_module', 'call_function', 'get_attr', 'output', 'root']
self.op = op # the kind of operation = placeholder|call_method|call_module|call_function|get_attr
if op == 'call_function':
if not callable(target):
raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
'but a Callable is expected')
else:
if not isinstance(target, str):
raise ValueError(f'Node [graph = {graph}, name = \'{name}\'] target {target} has type {torch.typename(target)} '
'but a str is expected')
self.target = target # for method/module/function, the name of the method/module/function/attr
# being invoked, e.g add, layer1, or torch.add
# All `Node`-valued inputs. Key is the Node, value is don't-care.
# The public API for this is `all_input_nodes`, this private attribute
# should not be accessed directly.
self._input_nodes : Dict[Node, None] = {}
self.__update_args_kwargs(map_arg(args, lambda x: x), map_arg(kwargs, lambda x: x)) # type: ignore[arg-type]
# All of the nodes that use the value produced by this Node
# Note one user may correspond to several uses, e.g. the node fo ``x + x``
# would appear once here, but represents two uses.
#
# Is a dict to act as an "ordered set". Keys are significant, value dont-care
self.users : Dict['Node', None] = {}
# Type expression representing the output value of this node.
# This should contain the same class of Type objects that would appear
# as type annotations for function inputs/outputs.
#
# For placeholder nodes, this value will be used to type-annotate the
# generated function parameters.
# For the return node, this value will be used to type-annotate the
# generated function return type. (Note this is a special case. ``return``
# does not produce a value, it's more of a notation. Thus, this value
# describes the type of args[0] in the ``return`` node.
self.type : Optional[Any] = return_type
self._prev = self
self._next = self
self._erased = False
# If set, use this fn to print this node
self._repr_fn : Optional[Callable[[Node], str]] = None
self._stack_trace : Optional[str] = None
# Dictionary to store metadata passes need to do their
# transformations. This metadata is preserved across node copies
self.meta : Dict[str, Any] = {}
@property
def next(self) -> 'Node':
"""
Returns the next ``Node`` in the linked list of Nodes.
Returns:
The next ``Node`` in the linked list of Nodes.
"""
return self._next
@property
def prev(self) -> 'Node':
"""
Returns the previous ``Node`` in the linked list of Nodes.
Returns:
The previous ``Node`` in the linked list of Nodes.
"""
return self._prev
[docs] def prepend(self, x: 'Node') -> None:
"""
Insert x before this node in the list of nodes in the graph. Example::
Before: p -> self
bx -> x -> ax
After: p -> x -> self
bx -> ax
Args:
x (Node): The node to put before this node. Must be a member of the same graph.
"""
assert self.graph == x.graph, "Attempting to move a Node into a different Graph"
x._remove_from_list()
p = self._prev
p._next, x._prev = x, p
x._next, self._prev = self, x
[docs] def append(self, x: 'Node') -> None:
"""
Insert x after this node in the list of nodes in the graph.
Equvalent to ``self.next.prepend(x)``
Args:
x (Node): The node to put after this node. Must be a member of the same graph.
"""
self._next.prepend(x)
def _remove_from_list(self):
p, n = self._prev, self._next
p._next, n._prev = n, p
@property
def args(self) -> Tuple[Argument, ...]:
"""
The tuple of arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment.
"""
return self._args
@args.setter
def args(self, a : Tuple[Argument, ...]):
"""
Set the tuple of arguments to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information.
"""
# DO NOT CALL `__update_args_kwargs` directly. The correct way to
# set `args` is via direct assignment, i.e. `node.args = new_args`
self.__update_args_kwargs(map_arg(a, lambda x: x), self._kwargs) # type: ignore[arg-type]
@property
def kwargs(self) -> Dict[str, Argument]:
"""
The dict of keyword arguments to this ``Node``. The interpretation of arguments
depends on the node's opcode. See the :class:`Node` docstring for more
information.
Assignment to this property is allowed. All accounting of uses and users
is updated automatically on assignment.
"""
return self._kwargs
@kwargs.setter
def kwargs(self, k : Dict[str, Argument]):
"""
Set the dict of kwargs to this Node. The interpretation of arguments
depends on the node's opcode. See the ``fx.Graph`` docstring for more
information.
"""
# DO NOT CALL `__update_args_kwargs` directly. The correct way to
# set `args` is via direct assignment, i.e. `node.kwargs = new_kwargs`
self.__update_args_kwargs(self._args, map_arg(k, lambda x: x)) # type: ignore[arg-type]
@property
def all_input_nodes(self) -> List['Node']:
"""
Return all Nodes that are inputs to this Node. This is equivalent to
iterating over ``args`` and ``kwargs`` and only collecting the values that
are Nodes.
Returns:
List of ``Nodes`` that appear in the ``args`` and ``kwargs`` of this
``Node``, in that order.
"""
return list(self._input_nodes.keys())
[docs] def update_arg(self, idx : int, arg : Argument) -> None:
"""
Update an existing positional argument to contain the new value
``arg``. After calling, ``self.args[idx] == arg``.
Args:
idx (int): The index into ``self.args`` of the element to update
arg (Argument): The new argument value to write into ``args``
"""
args = list(self.args)
args[idx] = arg
self.args = tuple(args)
[docs] def update_kwarg(self, key : str, arg : Argument) -> None:
"""
Update an existing keyword argument to contain the new value
``arg``. After calling, ``self.kwargs[key] == arg``.
Args:
key (str): The key in ``self.kwargs`` of the element to update
arg (Argument): The new argument value to write into ``kwargs``
"""
kwargs = dict(self.kwargs)
kwargs[key] = arg
self.kwargs = kwargs
@property
def stack_trace(self) -> Optional[str]:
"""
Return the Python stack trace that was recorded during tracing, if any.
This property is usually populated by `Tracer.create_proxy`. To record
stack traces during tracing for debug purposes, set
`record_stack_traces = True` on the `Tracer` instance.
"""
return self._stack_trace
@stack_trace.setter
def stack_trace(self, trace : Optional[str]):
self._stack_trace = trace
def __update_args_kwargs(self, new_args : Tuple['Argument', ...], new_kwargs : Dict[str, 'Argument']):
"""
This API is internal. Do *not* call it directly.
"""
self._args = new_args
self._kwargs = new_kwargs
for old_use in self._input_nodes.keys():
old_use.users.pop(self)
self._input_nodes = {}
map_arg(self._args, lambda n: self._input_nodes.setdefault(n))
map_arg(self._kwargs, lambda n: self._input_nodes.setdefault(n))
for new_use in self._input_nodes.keys():
new_use.users.setdefault(self)
def __repr__(self) -> str:
if self._repr_fn:
return self._repr_fn(self)
return self.name
def _pretty_print_target(self, target):
"""
Make target printouts more user-friendly.
1) builtins will be printed as `builtins.xyz`
2) operators will be printed as `operator.xyz`
3) other callables will be printed with qualfied name, e.g. torch.add
"""
if isinstance(target, str):
return target
if hasattr(target, '__module__'):
if not hasattr(target, '__name__'):
# Just to be defensive, if we don't have `__name__`, get the
# qualname. Not sure if this happens for any members of `operator`
# or `builtins`. This fallback path is not as good, since e.g.
# things in `operator` have `_operator` as their __module__.
return _get_qualified_name(target)
if target.__module__ == 'builtins':
return f'builtins.{target.__name__}'
elif target.__module__ == '_operator':
return f'operator.{target.__name__}'
return _get_qualified_name(target)
[docs] def format_node(self,
placeholder_names: List[str] = None,
maybe_return_typename: List[str] = None) -> Optional[str]:
"""
Return a descriptive string representation of ``self``.
This method can be used with no arguments as a debugging
utility.
This function is also used internally in the ``__str__`` method
of ``Graph``. Together, the strings in ``placeholder_names``
and ``maybe_return_typename`` make up the signature of the
autogenerated ``forward`` function in this Graph's surrounding
GraphModule. ``placeholder_names`` and ``maybe_return_typename``
should not be used otherwise.
Args:
placeholder_names: A list that will store formatted strings
representing the placeholders in the generated
``forward`` function. Internal use only.
maybe_return_typename: A single-element list that will store
a formatted string representing the output of the
generated ``forward`` function. Internal use only.
Returns:
str: If 1) we're using ``format_node`` as an internal helper
in the ``__str__`` method of ``Graph``, and 2) ``self``
is a placeholder Node, return ``None``. Otherwise,
return a descriptive string representation of the
current Node.
"""
if self.op == 'placeholder':
assert isinstance(self.target, str)
arg_str = self.target
arg_str += arg_str + f': {_type_repr(self.type)}' if self.type else ''
if placeholder_names:
placeholder_names.append(arg_str)
return None
maybe_typename = f'{_type_repr(self.type)} ' if self.type else ''
default_val = '(default=' + str(self.args[0]) + ')' if self.args else ''
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = {self.op}[target={self.target}]{default_val}'
elif self.op == 'get_attr':
maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = ' \
f'{self.op}[target={self._pretty_print_target(self.target)}]'
elif self.op == 'output':
if self.type and maybe_return_typename:
maybe_return_typename[0] = f' -> {_type_repr(self.type)}'
return f'return {self.args[0]}'
else:
maybe_typename = f'{_type_repr(self.type)} ' if self.type is not None else ''
return f'%{self.name} : {maybe_typename}[#users={len(self.users)}] = ' \
f'{self.op}[target={self._pretty_print_target(self.target)}](' \
f'args = {_format_arg(self.args)}, kwargs = {_format_arg(self.kwargs)})'
[docs] def replace_all_uses_with(self, replace_with : 'Node') -> List['Node']:
"""
Replace all uses of ``self`` in the Graph with the Node ``replace_with``.
Args:
replace_with (Node): The node to replace all uses of ``self`` with.
Returns:
The list of Nodes on which this change was made.
"""
to_process = list(self.users)
for use_node in to_process:
def maybe_replace_node(n : Node) -> Node:
if n == self:
return replace_with
else:
return n
new_args = map_arg(use_node.args, maybe_replace_node)
new_kwargs = map_arg(use_node.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
use_node.__update_args_kwargs(new_args, new_kwargs)
assert len(self.users) == 0
return to_process
[docs] def is_impure(self):
"""
Returns whether this op is impure, i.e. if its op is a placeholder or
output, or if a call_function or call_module which is impure.
Returns:
bool: If the op is impure or not.
"""
if self.op in {"placeholder", "output"}:
return True
# Check if an impure function.
if self.op == "call_function":
return self.target in _side_effectful_functions
# Check if an impure module.
if self.op == "call_module":
assert (
self.graph.owning_module is not None
), "self.graph.owning_module not set for purity check"
target_mod = self.graph.owning_module.get_submodule(self.target)
assert (
target_mod is not None
), f"Did not find expected submodule target {self.target}"
return getattr(target_mod, "_is_impure", False)
return False
[docs] def normalized_arguments(
self, root : torch.nn.Module, arg_types : Optional[Tuple[Any]] = None,
kwarg_types : Optional[Dict[str, Any]] = None,
normalize_to_only_use_kwargs : bool = False) -> Optional[ArgsKwargsPair]:
"""
Returns normalized arguments to Python targets. This means that
`args/kwargs` will be matched up to the module/functional's
signature and return exclusively kwargs in positional order
if `normalize_to_only_use_kwargs` is true.
Also populates default values. Does not support positional-only
parameters or varargs parameters.
Supports module calls.
May require `arg_types` and `kwarg_types` in order to disambiguate overloads.
Args:
root (torch.nn.Module): Module upon which to resolve module targets.
arg_types (Optional[Tuple[Any]]): Tuple of arg types for the args
kwarg_types (Optional[Dict[str, Any]]): Dict of arg types for the kwargs
normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
Returns:
Returns NamedTuple ArgsKwargsPair, or `None` if not successful.
"""
if self.op == 'call_function':
assert callable(self.target)
return normalize_function(self.target, self.args, self.kwargs, arg_types, kwarg_types) # type: ignore[arg-type]
elif self.op == 'call_module':
assert isinstance(self.target, str)
return normalize_module(root, self.target, self.args, self.kwargs) # type: ignore[arg-type]
return None
[docs] def replace_input_with(self, old_input: 'Node', new_input: 'Node'):
"""
Loop through input nodes of ``self``, and replace all instances of
``old_input`` with ``new_input``.
Args:
old_input (Node): The old input node to be replaced.
new_input (Node): The new input node to replace ``old_input``.
"""
def maybe_replace_node(n : Node) -> Node:
return new_input if n == old_input else n
new_args = map_arg(self.args, maybe_replace_node)
new_kwargs = map_arg(self.kwargs, maybe_replace_node)
assert isinstance(new_args, tuple)
assert isinstance(new_kwargs, dict)
self.__update_args_kwargs(new_args, new_kwargs)
def map_arg(a: Argument, fn: Callable[[Node], Argument]) -> Argument:
""" Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. """
assert callable(fn), "torch.fx.map_arg(a, fn): fn must be a callable"
return map_aggregate(a, lambda x: fn(x) if isinstance(x, Node) else x)
def map_aggregate(a: Argument, fn: Callable[[Argument], Argument]) -> Argument:
""" Apply fn to each Node appearing arg. arg may be a list, tuple, slice, or dict with string keys. """
if isinstance(a, tuple):
return tuple(map_aggregate(elem, fn) for elem in a)
elif isinstance(a, list):
return immutable_list(map_aggregate(elem, fn) for elem in a)
elif isinstance(a, dict):
return immutable_dict((k, map_aggregate(v, fn)) for k, v in a.items())
elif isinstance(a, slice):
return slice(map_aggregate(a.start, fn), map_aggregate(a.stop, fn), map_aggregate(a.step, fn))
else:
return fn(a)
